Protein Secondary Structure Prediction Using Large Margin Methods

Protein secondary structure prediction is an important step to understanding protein tertiary structure. Recent studies indicate that the correlation between neighboring secondary structures are beneficial to improve prediction performance. In this paper, we propose a new large margin approach for protein secondary structure prediction, which consider the problem as a sequence labeling problem like probabilistic graphical models. It doesnpsilat only make full use of the correlation between neighboring secondary structures like graphical chain models, but also shares the key advantages of other SVM-based methods, i.e. learning non-linear discriminate via kernel functions. The experimental results on datasets: CB513 and RS126 show that our algorithm outperforms other state-of-the-art methods.